自激索赔模式下的最佳索赔比例再保险

IF 1.6 3区 数学 Q2 MATHEMATICS, APPLIED
Fan Wu, Yang Shen, Xin Zhang, Kai Ding
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引用次数: 0

摘要

本文研究的是具有自激索赔的保险公司的最优再保险问题,在这种情况下,保险人的历史索赔会影响索赔强度本身。我们将重点放在与索赔相关的比例再保险接触上,这里的 "与索赔相关 "是指允许保险公司的风险自留比例取决于索赔规模。保险人的目标是最大化终端财富的预期效用。利用动态编程原理和验证定理,我们从指数效用函数下的汉密尔顿-雅各比-贝尔曼方程中得到了最优再保险策略和相应的闭式价值函数。我们证明,在指数效用最大化准则下,依赖索赔的比例再保险是所有类型再保险中的最优选择。此外,我们还介绍了推导出的最优策略的若干分析性质和数值示例,并通过分析和数值分析提供了经济学见解。我们特别指出,最优的依赖索赔的比例再保险可视为保险人与再保险人之间分步风险分担规则的连续近似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Optimal Claim-Dependent Proportional Reinsurance Under a Self-Exciting Claim Model

Optimal Claim-Dependent Proportional Reinsurance Under a Self-Exciting Claim Model

This paper investigates an optimal reinsurance problem for an insurance company with self-exciting claims, where the insurer’s historical claims affect the claim intensity itself. We focus on a claim-dependent proportional reinsurance contact, where the term “claim-dependent” signifies that the insurer’s risk retention ratio is allowed to depend on claim size. The insurer aims to maximize the expected utility of terminal wealth. By utilizing the dynamic programming principle and verification theorem, we obtain the optimal reinsurance strategy and corresponding value function in closed-form from the Hamilton–Jacobi–Bellman equation under an exponential utility function. We show that the claim-dependent proportional reinsurance is optimal among all types of reinsurance under the exponential utility maximization criterion. In addition, we present several analytical properties and numerical examples of the derived optimal strategy and provide economic insights through analytical and numerical analyses. In particular, we show the optimal claim-dependent proportional reinsurance can be considered as a continuous approximation of the step-wise risk sharing rule between the insurer and the reinsurer.

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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
149
审稿时长
9.9 months
期刊介绍: The Journal of Optimization Theory and Applications is devoted to the publication of carefully selected regular papers, invited papers, survey papers, technical notes, book notices, and forums that cover mathematical optimization techniques and their applications to science and engineering. Typical theoretical areas include linear, nonlinear, mathematical, and dynamic programming. Among the areas of application covered are mathematical economics, mathematical physics and biology, and aerospace, chemical, civil, electrical, and mechanical engineering.
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